This assignment is for ETC5521 Assignment 1 by Team EMU comprising of Min Min Soh and Rohan Baghel.
Global fishing receives a great deal of attention in the media for the past decades. The rise of world population has increased the demand for seafood across the world. Coastal countries mainly rely on fishing as one of the most important food sources. Our first research question would learn about seafood consumption of countries over time. We’ll also study about how much seafood are being produced by each country overtime.
Meanwhile, the health of fish population in the world remains as a concern with the rise of global fishing. It then prompts us to assess how much of fish stocks are caught within the sustainable levels over the years without over exploiting the fish populations.
Fish farming (or ‘aquaculture’)helps to contribute to the seafood production while alleviating the pressure of wild fisheries. This inspires us to further analyse the trend of aquaculture as compared to wild fish catch over time.
We begin by describing the data in the next section, how we source it and how we prepare the data for analysis. In the analysis section, we present our observations through graphical displays. Our main tool is R, a programming language for statistical computing and graphics.
The data set has been obtained from tidytuesday r package or through the website https://ourworldindata.org. The data comprises of four files in the “.csv” format which is machine readable and can be used to analyze the state of fish production and consumption in the world.
The data dictionary for the data set has been given below. They define the variables and their types in each of the data sets.
capture-fisheries-vs-aquaculture.csv| variable | class | description |
|---|---|---|
| Entity | character | Country/entity |
| Code | character | Country code (see countrycode R package) |
| Year | double | Year |
| Aquaculture production (metric tons) | double | Production of aquaculture animals |
| Capture fisheries production (metric tons) | double | Captured aquaculture |
fish-and-seafood-consumption-per-capita.csv| variable | class | description |
|---|---|---|
| Entity | character | Country/entity |
| Code | character | Country code (see countrycode R package) |
| Year | double | Year |
| Fish, Seafood- Food supply quantity (kg/capita/yr) (FAO, 2020) | double | Food supply in fish in kg/capita/year |
fish-stocks-within-sustainable-levels.csv| variable | class | description |
|---|---|---|
| Entity | character | Country/entity |
| Code | character | Country code (see countrycode R package) |
| Year | double | Year |
| Share of fish stocks within biologically sustainable levels (FAO, 2020) | double | Share of sustainable fish stock |
| Share of fish stocks that are overexploited | double | Share of fish stock that are overexploited |
seafood-and-fish-production-thousand-tonnes.csv| variable | class | description |
|---|---|---|
| Entity | character | Country/entity |
| Code | character | Country code (see countrycode R package) |
| Year | double | . |
| Pelagic Fish - 2763 - Production - 5510 - tonnes | double | Pelagic Fish |
| Crustaceans - 2765 - Production - 5510 - tonnes | double | Crustaceans |
| Cephalopods - 2766 - Production - 5510 - tonnes | double | Cephalopods |
| Demersal Fish - 2762 - Production - 5510 - tonnes | double | Demersal |
| Freshwater Fish - 2761 - Production - 5510 - tonnes | double | Freshwater |
| Molluscs, Other - 2767 - Production - 5510 - tonnes | double | Molluscs |
| Marine Fish, Other - 2764 - Production - 5510 - tonnes | double | Marine |
Q1 What is the contribution of each production sector in global fishery from 1950 ?
Q2 What is the contribution of each country in the global fishery sector ?
Q3 What is the share of type of fishes produced in each country ?
Q4 What is the production level of each country by capturing over time ?
Q5 What is the production level of each country by farming over time ?
Q6 What has been the trend of seafood consumption of each country over the years ?
Q7 What has been the trend of captured vs farmed production of each country over the years ?
Q8 What has been the trend of sustainable levels of fish stocks in the world ?
Q9 What is the share of fishes of the that have been overexploited in the world over the years ?
Q10 How much of the fish stocks are maintained at sustainable levels in the world as compared to overall production level?
Q11 What is the production level of fish by each continent ?
Q12 What is the consumption level of fish by each continent ?
Q13 What can we learn about the uses of fish catch by countries?
Q14 What can we learn about the uses of fish catch over time?
Q15 Comparing seafood production to seafood consumption over time?
Q16 What can we learn about the sustainable levels of fishing as compared to farming
Q17 Would aquaculture alleviate the pressure of seafood consumption over time?
Q18 What can we observe about the seafood consumption in coastal countries and landlocked countries over time?
Q19 What can we observe about the level of seafood being discarded in the world across the years?
Q20 How much fresh water produce in each country over time ?
What has been the trend of seafood consumption of each country over the years ?
The expectation is We would expect that there is an increasing trend of seafood consumption levels over the years given that the rise of population.
Coastal countries consumed more seafood than landlocked countries.
What has been the trend of captured vs farmed production of each country over the years ?
We would expect that the aquaculture to be increasing over the years.
The expectation is high technology countries to contribute more to the aquaculture.
What is the contribution of each country in the global fishery sector ?
The expectation is that of growing trend in fishing for each country for feeding the increasing populace.
Countries that are landlocked or that do not have a big coastline will be contributing less to the fishery sector.
What has been the trend of sustainable levels of fish stocks in the world ?
An increase in the trend of over-exploitation of fish stocks all over the world.
A decreasing trend in the biologically sustainable levels of fish stocks all over the world.
We begin start with the evolution of the average seafood consumption in the world over the years.
Figure 5.1: Average seafood consumption in the world over time
Figure 5.1 shows the increasing trend in world seafood consumption from 1961 and peaked in 1989. However, the figure declined between 1990 and 1992, mainly due to economic difficulties in the low-income countries, such as Africa, Latin America, the Caribbean and the Near East East. This led to an increased pressure on the price of many products (Dumas, 1992). After the crisis has recovered, seafood consumption has increased throughout the world. Overall, this is consistent with our expectation of the increasing popularity of seafood consumption.
We will now investigate the patterns for all countries by partitioning the data based on the different nations.
Our analysis will be of the 10 countries with the highest average consumption and the 10 countries and the 10 countries with the lowest average consumption. As the original dataset provided contains other regions, such as Central Africa Republic and Central America, we’ve performed an inner join with the dataset called iso3166 from the maps package to extract only countries relevant dataset.
Table 5.1 shows the 10 countries with the highest average consumption from 1961 to 2017. Table 5.2 contains the list of 10 countries with the lowest average consumption. These results are consistent with our expectations, where the 10 countries with the highest average consumption are coastal countries. Seafood is frequently the primary source of food and employment in coastal countries.
| Entity | Average Consumption (kg) per person per capita | rank |
|---|---|---|
| MALDIVES | 120.85105 | 1 |
| ICELAND | 84.61667 | 2 |
| KIRIBATI | 68.23930 | 3 |
| JAPAN | 61.34737 | 4 |
| HONG KONG | 55.16842 | 5 |
| PORTUGAL | 53.56579 | 6 |
| NORWAY | 46.02509 | 7 |
| MALAYSIA | 44.68544 | 8 |
| SOLOMON ISLANDS | 44.43421 | 9 |
| ANTIGUA AND BARBUDA | 42.99000 | 10 |
| Entity | Average Consumption (kg) per person per capita | rank |
|---|---|---|
| AFGHANISTAN | 0.0782456 | 1 |
| ETHIOPIA | 0.2260000 | 2 |
| TAJIKISTAN | 0.2892308 | 3 |
| MONGOLIA | 0.5438596 | 4 |
| LESOTHO | 0.6863158 | 5 |
| UZBEKISTAN | 0.7707692 | 6 |
| NEPAL | 0.9540351 | 7 |
| SUDAN | 1.0133333 | 8 |
| GUATEMALA | 1.1338596 | 9 |
| RWANDA | 1.2085965 | 10 |
In this section we explore the changes in seafood consumption over time in the 10 countries with the highest average seafood consumption. Recall that seafood consumption trend in the world where we observe a decrease in 1989, so we insert a vertical dashed line at year 1989 for comparison purposes.
Figure 5.2: Seafood consumption among the top 10 countries over time
Figure 5.3: Seafood consumption among the top 10 countries over time. This plot is the same as previous plot but it allows interative plot elements.
Figure 5.4: Individual plots of the seafood consumption over time among the top 10 countries
Figure 5.3 and figure 5.4 highlights an increasing trend from 1961 to 1989 for most countries except Portugal. Interestingly, Hong Kong and Malaysia display an increasing trend in 1989 whereas others show a decreasing trend , similar to figure 5.1. This is mainly associated with the improvements of economy in Asia in 1988 and price inflation remained moderate (Dumas, 1992).
Seafood consumption in Maldives is the highest and the trend fluctuates overtime. The trend is declining after 2010 due to overfishing, employment falling and higher fuel costs (Salinas, Van Doorn, & Redaelli, 2015). Solomon islands and Japan also show a declining trend, mainly due to the overfishing problem. Overall, these 10 countries display different results to our expectations. Although some countries demonstrate an increasing trend in seafood consumption, others show a decline in consumption.
Aquaculture also being known as fish and seafood farming acts as one of the primary source of protein as human population continues to expands to meet shortfalls in fish supplies. Aquaculture also plays an important role in employment opportunities.
We’ll be exploring the trend of aquaculture in the world over years as compared to wild fish captured.
Figure 5.5 illustrates that global wild fish catch remained relatively constant from year 2000 onwards whereas aquaculture has grown rapidly since 1980s surpassing wild fish catch in 2013. It is consistent with our expectation that aquaculture has developed increasingly over time.
Figure 5.5: Captured fishery production VS Aquaculture in the world
In this section, we are going to explore the countries which contribute significantly to aquaculture over the years. As data might not be available pre-2000s for some of the countries, we decided to focus on data after year 2000. Similar to before, we’ll be performing an inner join with the dataset called iso3166 from the maps package to extract only countries relevant dataset as the original dataset contains other regions as well.
Table 5.3 shows the world 10 largest aquaculture producers from year 2000 onwards, among which China is the runaway leader followed by Peru, Indonesia and United States. China accounts for around 56% of aquaculture in the world in 2015 as shown in the table 5.4. The comprehensive aquaculture extension system and the opening up in 1978 contribute to the development of aquaculture in China (Wang, Ji, & Zhang, 2020). This is consistent with our expectation where high technology countries develop more in aquaculture. However, a significant share of production in 2015 also came from the other Asia regions such as Indonesia, where aquaculture is largely based on small-scale, non-commercial and family-based operations (Subasinghe, Soto, & Jia, 2009).
| Entity | Average wild fish caught (metric tons) | Average aquaculture (metric tons) | rank |
|---|---|---|---|
| CHINA | 15,206,839 | 15,206,839 | 1 |
| PERU | 6,740,076 | 6,740,076 | 2 |
| INDONESIA | 5,491,442 | 5,491,442 | 3 |
| UNITED STATES | 5,246,058 | 5,246,058 | 4 |
| INDIA | 4,357,689 | 4,357,689 | 5 |
| JAPAN | 4,132,989 | 4,132,989 | 6 |
| CHILE | 3,499,792 | 3,499,792 | 7 |
| NORWAY | 2,584,042 | 2,584,042 | 8 |
| VIETNAM | 2,346,373 | 2,346,373 | 9 |
| PHILIPPINES | 2,203,608 | 2,203,608 | 10 |
| Entity | Year | Percentage relative to world production |
|---|---|---|
| CHINA | 2015 | 56.01 |
| INDONESIA | 2015 | 14.76 |
| INDIA | 2015 | 4.96 |
| VIETNAM | 2015 | 3.28 |
| PHILIPPINES | 2015 | 2.22 |
| NORWAY | 2015 | 1.30 |
| JAPAN | 2015 | 1.04 |
| CHILE | 2015 | 1.00 |
| UNITED STATES | 2015 | 0.40 |
| PERU | 2015 | 0.09 |
Figure 5.6: Individual plots of the captured fishery production VS Aquaculture over time among the top 10 countries
In figure 5.6, most countries display an increasing trend in aquaculture production, especially in the Asia region, including China, India and Indonesia. This pattern is consistent with our initial expectation. Some countries are still relying more on the fisheries compared to aquaculture at the recent stage, including Japan and United States.
The data from “production.csv” had to be wrangled to make the best use of the data-set. The variables had to be renamed and total fish production had to be calculated to find the top producers of fish in the world.
Figure 5.7: Top fish producers of the world
From the Figure 5.7) we can see the top producers in the world. If we exclude the continental observations and group of countries according to their economic development we can see that China,Japan,Peru and United States have been the top producers of the world. These countries have very large coastal regions, and are able to exploit them to their advantage. Another benefit of having a large coastal region is that these countries have high consumption of fish and most of their cuisine is based around fish.
Similarly, the countries that are at the bottom of the fish production table are the arid regions of the world. These countries include Lesotho, Kyrgyzstan, Tajikistan and Jordan. They have very few sources of water and mostly consume meat to supplement their diet.
These findings are within expectations of initial analysis. Countries having a very large coastal line are more dependent on fish for their diet and can even sell them in the international market while the land locked and dry regions have a very minuscule share of fish production in the world.
Figure 5.8: Fish Production in the World
From figure 5.8), we can observe the trend of fish production in the world according to the types of fishes that are being consumed around the world. “Freshwater fishes” and “pelagic fishes” make up the majority of fish production in the world while “marine fish” and “cephalopods” are at the bottom of fish production trend.
It is clear that the fish production has always shown a upward trend and the majority of fish that is being consumed around the world is from freshwater sources or from pelagic zones of the oceans.
This trend does not deviate from expectation, as the population growth will always demand more seafood for consumption. It is a little unexpected that the majority of the food production is based around freshwater fishes while marine fish are at the bottom of the table.
| Entity | Year | Sustainable_levels_Fish | Overexploited_Fish |
|---|---|---|---|
| World | 1974 | 90.00000 | 10.000000 |
| World | 1978 | 91.46341 | 8.536585 |
| World | 1979 | 86.98225 | 13.017751 |
| World | 1981 | 86.41975 | 13.580247 |
| World | 1983 | 83.33333 | 16.666667 |
| World | 1985 | 81.81818 | 18.181818 |
| World | 1987 | 75.67568 | 24.324324 |
| World | 1989 | 73.36957 | 26.630435 |
| World | 1990 | 81.86813 | 18.131868 |
| World | 1992 | 76.77725 | 23.222749 |
The data from “stock.csv” had to be wrangled to remove irrelevant observations and the names of the variables had to be changed to make the data-set more presentable and easy to use.
Figure 5.9: Fish stock of the world
From the Figure 5.9), we can observe the trend of fish exploitation and sustainable levels in the world. It is evident from the trend of from the plot that the sustainable level of fish has been going downwards for a very long time due to over exploitation of fish stocks.
Since these two variables complement each other , we can observe that when one increases the other decreases and vice versa.
Becker OScbRA, Minka ARWRvbRBEbTP, Deckmyn. A (2021). maps: Draw Geographical Maps. R package version 3.4.0, https://CRAN.R-project.org/package=maps.
C. Sievert. Interactive Web-Based Data Visualization with R, plotly, and shiny. Chapman and Hall/CRC Florida, 2020.
Dumas, M. W. (1992). Productivity trends: prepared fish and seafoods industry. Monthly Lab. Rev., 115, 3.
Hughes E (2022). tidytuesdayR: Access the Weekly ‘TidyTuesday’ Project Dataset. R package version 1.0.2, https://CRAN.R-project.org/package=tidytuesdayR.
H. Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016.
Ooms J (2022). gifski: Highest Quality GIF Encoder. R package version 1.6.6-1, https://CRAN.R-project.org/package=gifski.
Pedersen T, Robinson D (2020). gganimate: A Grammar of Animated Graphics. R package version 1.0.7, https://CRAN.R-project.org/package=gganimate.
Pedersen T (2020). patchwork: The Composer of Plots. R package version 1.1.1, https://CRAN.R-project.org/package=patchwork.
Pedersen T (2020). transformr: Polygon and Path Transformations. R package version 0.1.3, https://CRAN.R-project.org/package=transformr.
Salinas, E., Van Doorn, R., & Redaelli, S. (2015). Maldives: Identifying Opportunities and Constraints to Ending Poverty and Promoting Shared Prosperity.
Simon Garnier, Noam Ross, Robert Rudis, Antônio P. Camargo, Marco Sciaini, and Cédric Scherer (2021). Rvision - Colorblind-Friendly Color Maps for R. R package version 0.6.2.
Simon Garnier, Noam Ross, Robert Rudis, Antônio P. Camargo, Marco Sciaini, and Cédric Scherer (2021). Rvision - Colorblind-Friendly Color Maps for R.
Subasinghe, R., Soto, D., & Jia, J. (2009). Global aquaculture and its role in sustainable development. Reviews in aquaculture, 1(1), 2-9.
Tierney N (2017). “visdat: Visualising Whole Data Frames.” JOSS, 2(16), 355. doi:10.21105/joss.00355 https://doi.org/10.21105/joss.00355, http://dx.doi.org/10.21105/joss.00355.
Urbanek S (2013). png: Read and write PNG images. R package version 0.1-7, https://CRAN.R-project.org/package=png.
Wang, P., Ji, J., & Zhang, Y. (2020). Aquaculture extension system in China: development, challenges, and prospects. Aquaculture reports, 17, 100339.
Wickham H, Averick M, Bryan J, Chang W, McGowan LD, François R, Grolemund G, Hayes A, Henry L, Hester J, Kuhn M, Pedersen TL, Miller E, Bache SM, Müller K, Ooms J, Robinson D, Seidel DP, Spinu V, Takahashi K, Vaughan D, Wilke C, Woo K, Yutani H (2019). “Welcome to the tidyverse.” Journal of Open Source Software, 4(43), 1686. doi:10.21105/joss.01686 https://doi.org/10.21105/joss.01686.
Wickham H, Hester J, Bryan J (2022). readr: Read Rectangular Text Data. R package version 2.1.2, https://CRAN.R-project.org/package=readr.
Wilke C (2021). ggridges: Ridgeline Plots in ‘ggplot2’. R package version 0.5.3, https://CRAN.R-project.org/package=ggridges.
Yihui Xie (2022). bookdown: Authoring Books and Technical Documents with R Markdown. R package version 0.26.
Zhu H (2021). kableExtra: Construct Complex Table with ‘kable’ and Pipe Syntax. R package version 1.3.4, https://CRAN.R-project.org/package=kableExtra.
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## transformr * 0.1.3 2020-07-05 [1] CRAN (R 4.2.0)
## tweenr 1.0.2 2021-03-23 [1] CRAN (R 4.2.0)
## tzdb 0.3.0 2022-03-28 [1] CRAN (R 4.2.0)
## units 0.8-0 2022-02-05 [1] CRAN (R 4.2.0)
## usethis 2.1.6 2022-05-25 [1] CRAN (R 4.2.0)
## utf8 1.2.2 2021-07-24 [1] CRAN (R 4.2.0)
## vctrs 0.4.1 2022-04-13 [1] CRAN (R 4.2.0)
## viridis * 0.6.2 2021-10-13 [1] CRAN (R 4.2.0)
## viridisLite * 0.4.0 2021-04-13 [1] CRAN (R 4.2.0)
## visdat * 0.5.3 2019-02-15 [1] CRAN (R 4.2.0)
## vroom 1.5.7 2021-11-30 [1] CRAN (R 4.2.0)
## webshot 0.5.3 2022-04-14 [1] CRAN (R 4.2.0)
## withr 2.5.0 2022-03-03 [1] CRAN (R 4.2.0)
## xfun 0.30 2022-03-02 [1] CRAN (R 4.2.0)
## xml2 1.3.3 2021-11-30 [1] CRAN (R 4.2.0)
## yaml 2.3.5 2022-02-21 [1] CRAN (R 4.2.0)
##
## [1] /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library
##
## ──────────────────────────────────────────────────────────────────────────────